Utilization of physician services for diabetic patients from ethnic minorities
Bibliographic record
Abstract
BACKGROUND: Diabetes is a common chronic disease, which results in significant morbidity and mortality. Although ethnic variations in disease prevalence are known, variations in the utilization of physician services for the disease (particularly in publicly funded health care systems) are uncertain. METHODS: Self-reported ethnicity was determined from two population health surveys in Ontario, Canada. These data were linked to administrative data sources, including an administrative data-derived disease registry. Diabetes prevalence was determined for each ethnic group. Utilization of physician services for primary care, diabetes specialist care and eye examinations was compared among ethnic groups, adjusting for age, sex, socioeconomic status and diabetes duration. RESULTS: There were 20,788 eligible survey respondents. Standardized diabetes prevalence was elevated for the South Asian and Black populations (11.1 and 11.0%, respectively) compared with that for the White population (5.9%). Ethnic minorities with diabetes were less likely to receive an eye examination compared with White patients (adjusted OR, 0.63; 95% CI, 0.46-0.85). The use of primary care and diabetes specialist care did not differ. CONCLUSION: Ethnic minorities with diabetes are less likely to receive eye examinations. This disparity in quality of care could lead to worse clinical outcomes for these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".